{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7d23af1e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cuda available\n",
      "PyTorch version:  2.0.1\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from simpletransformers.classification import ClassificationModel\n",
    "from simpletransformers.classification import ClassificationArgs\n",
    "from sklearn.metrics import f1_score, accuracy_score, roc_auc_score, recall_score, precision_score, precision_recall_curve, auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch\n",
    "import wandb\n",
    "import logging\n",
    "from datasets import Dataset, DatasetDict\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
    "from sklearn.metrics import balanced_accuracy_score, precision_recall_fscore_support, accuracy_score, classification_report\n",
    "\n",
    "# Verify that GPU is being used\n",
    "print(\"Cuda available\" if torch.cuda.is_available() is True else \"CPU\")\n",
    "print(\"PyTorch version: \", torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5c5e9e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import training and testing data\n",
    "train_df = pd.read_csv('./trump_train_data.csv')\n",
    "test_df = pd.read_csv('./trump_twitter_supervised.csv')\n",
    "\n",
    "# subset and format for the trainer\n",
    "train_df.rename(mapper={'stance_sup':'labels'}, axis = 1, inplace = True)\n",
    "train_df = train_df[['text', 'labels']]\n",
    "# split training data to train and validation set\n",
    "train_set, val_set = train_test_split(train_df, test_size=0.2, random_state=1)\n",
    "\n",
    "test_df.rename(mapper={'adjudicated_sup':'labels'}, axis = 1, inplace = True)\n",
    "test_df = test_df[['text', 'labels']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67b8f41d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmlburnham\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wandb.login()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b47cbe5f-57ec-4dde-8256-d4c5bb63afe8",
   "metadata": {},
   "source": [
    "# PoliBERTweet Sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c14fdcfe-f55f-47b0-81f1-ecf48e3d9c06",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create sweep with ID: eo1fu2un\n",
      "Sweep URL: https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: mpy2z8yz with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 4.0100917896506814e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 2\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.12"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231103_223133-mpy2z8yz</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/mpy2z8yz' target=\"_blank\">fiery-sweep-1</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View sweep at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/mpy2z8yz' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/runs/mpy2z8yz</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at kornosk/polibertweet-political-twitter-roberta-mlm and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▃▃▁▅█</td></tr><tr><td>accuracy</td><td>▁▆▆▆▇▇▇▇▇█</td></tr><tr><td>auprc</td><td>▁▃▃▅▅▆▇███</td></tr><tr><td>auroc</td><td>▁▄▃▆▅▆▇██▇</td></tr><tr><td>eval_loss</td><td>█▂▅▁▂▁▄▃▅▆</td></tr><tr><td>fn</td><td>▁▅█▄▆▄▅▅▅▆</td></tr><tr><td>fp</td><td>█▃▁▃▂▃▂▂▂▁</td></tr><tr><td>global_step</td><td>▁▂▂▃▃▄▄▅▅▅▆▆▇██</td></tr><tr><td>lr</td><td>█▆▅▃▁</td></tr><tr><td>mcc</td><td>▁▆▅▅▆▆▆▇▇█</td></tr><tr><td>tn</td><td>▁▆█▆▇▆▇▇▇█</td></tr><tr><td>tp</td><td>█▄▁▅▃▅▄▄▄▃</td></tr><tr><td>train_loss</td><td>▇▃▃▆▃▃▃▁▁█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.9234</td></tr><tr><td>accuracy</td><td>0.85849</td></tr><tr><td>auprc</td><td>0.86672</td></tr><tr><td>auroc</td><td>0.90574</td></tr><tr><td>eval_loss</td><td>0.49307</td></tr><tr><td>fn</td><td>46</td></tr><tr><td>fp</td><td>29</td></tr><tr><td>global_step</td><td>266</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.68706</td></tr><tr><td>tn</td><td>312</td></tr><tr><td>tp</td><td>143</td></tr><tr><td>train_loss</td><td>0.73392</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 56qkpi3t with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 2\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/56qkpi3t' target=\"_blank\">brisk-sweep-2</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un</a>"
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at kornosk/polibertweet-political-twitter-roberta-mlm and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▅▃▁▄█</td></tr><tr><td>accuracy</td><td>▁▇████████</td></tr><tr><td>auprc</td><td>▁▆▆▆▇█████</td></tr><tr><td>auroc</td><td>▁▇▆▇██████</td></tr><tr><td>eval_loss</td><td>█▄▃▁▁▁▁▁▁▂</td></tr><tr><td>fn</td><td>▁▄█▃▆▅▅▆▅▇</td></tr><tr><td>fp</td><td>█▂▁▂▁▁▂▁▁▁</td></tr><tr><td>global_step</td><td>▁▂▂▃▃▄▄▅▅▅▆▆▇██</td></tr><tr><td>lr</td><td>█▆▅▃▁</td></tr><tr><td>mcc</td><td>▁▇████████</td></tr><tr><td>tn</td><td>▁▇█▇██▇███</td></tr><tr><td>tp</td><td>█▅▁▆▃▄▄▃▄▂</td></tr><tr><td>train_loss</td><td>█▄▄▆▄▃▄▂▁▆</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.86231</td></tr><tr><td>accuracy</td><td>0.83019</td></tr><tr><td>auprc</td><td>0.86922</td></tr><tr><td>auroc</td><td>0.91036</td></tr><tr><td>eval_loss</td><td>0.39737</td></tr><tr><td>fn</td><td>42</td></tr><tr><td>fp</td><td>48</td></tr><tr><td>global_step</td><td>266</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.63273</td></tr><tr><td>tn</td><td>293</td></tr><tr><td>tp</td><td>147</td></tr><tr><td>train_loss</td><td>0.56771</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: wzctkf9v with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 5\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231103_223738-wzctkf9v</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/wzctkf9v' target=\"_blank\">upbeat-sweep-3</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un</a>"
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at kornosk/polibertweet-political-twitter-roberta-mlm and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▃▃▂▄█▁▄▃▂▁▁▁▂</td></tr><tr><td>accuracy</td><td>▁▇▇▅▇▇▇█▆▇▇▇▇▇▇▇█▆▇▇▇▇▇▇▇▇▇</td></tr><tr><td>auprc</td><td>▁▃▂▄▅▆█▇▃▄▄▅▅██▇▅▆▄██▇▇▇▇▇▇</td></tr><tr><td>auroc</td><td>▁▄▃▅▄▅▇█▃▇▇▆▆▇▇▇▆▅▄▆▆▅▅▅▆▅▅</td></tr><tr><td>eval_loss</td><td>▃▁▂▁▁▁▂▂▅▂▂▄▅▂▅▃▄▆█▆▆▇█▇▇▇▇</td></tr><tr><td>fn</td><td>▁▅▆▃▅▄▅▄█▃▄▅▆▃▅▄▅▆▇▄▄▆▆▅▅▅▅</td></tr><tr><td>fp</td><td>█▃▂▄▂▃▂▂▁▃▂▂▁▃▂▃▂▂▁▂▂▂▁▂▂▂▂</td></tr><tr><td>global_step</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇████</td></tr><tr><td>lr</td><td>█▇▇▆▆▅▅▄▃▃▂▂▁</td></tr><tr><td>mcc</td><td>▁▆▆▄▆▇▇█▅▇▇▇▆▆▇▆▇▅▅▇▇▆▆▆▆▆▆</td></tr><tr><td>tn</td><td>▁▇▇▅▇▇▇▇█▆▇▇█▆▇▆▇▇█▇▇▇▇▇▇▇▇</td></tr><tr><td>tp</td><td>█▅▃▆▄▅▄▅▁▆▅▄▃▆▄▅▄▃▂▅▅▃▃▄▄▄▄</td></tr><tr><td>train_loss</td><td>█▅▄▇▄▅▆▂▁▇▅▂▅▄▆▁▁▃▁▁▁▁▁▁▁▁▄</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.16345</td></tr><tr><td>accuracy</td><td>0.83396</td></tr><tr><td>auprc</td><td>0.8595</td></tr><tr><td>auroc</td><td>0.8987</td></tr><tr><td>eval_loss</td><td>0.85064</td></tr><tr><td>fn</td><td>49</td></tr><tr><td>fp</td><td>39</td></tr><tr><td>global_step</td><td>665</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.6344</td></tr><tr><td>tn</td><td>302</td></tr><tr><td>tp</td><td>140</td></tr><tr><td>train_loss</td><td>0.221</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: jlc4nwuv with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 4\n",
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/jlc4nwuv' target=\"_blank\">upbeat-sweep-4</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un</a>"
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       " View project at <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2</a>"
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       " View sweep at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un</a>"
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       " View run at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/jlc4nwuv' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/runs/jlc4nwuv</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at kornosk/polibertweet-political-twitter-roberta-mlm and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
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      "INFO:simpletransformers.classification.classification_utils: Saving features into cached file cache_dir/cached_train_bertweet_512_2_2\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▃▄▃▄█▁▃▄▁▃</td></tr><tr><td>accuracy</td><td>▁▇▇▇▇███▇█▇██▇█▇▇████</td></tr><tr><td>auprc</td><td>▁▄▃▄▅▅▅▆▆▇▇█▆▇▆▇▅▆▇▇█</td></tr><tr><td>auroc</td><td>▁▃▃▅▆▆▄█▆▇▇▇▅▇▆▇▅▆▆▆▇</td></tr><tr><td>eval_loss</td><td>▄▂▃▁▁▁▃▂▄▃▃▅▆▃▆▄▆▇█▇▇</td></tr><tr><td>fn</td><td>▁▆█▄▄▅▅▄▆▅▅▅▆▅▆▅▆▅▆▅▅</td></tr><tr><td>fp</td><td>█▂▁▃▃▂▂▂▂▂▂▂▁▂▁▂▁▂▁▂▂</td></tr><tr><td>global_step</td><td>▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇███</td></tr><tr><td>lr</td><td>█▇▆▆▅▄▃▃▂▁</td></tr><tr><td>mcc</td><td>▁▇▆▆▆▇██▇▇▇██▇▇▇▇▇▇█▇</td></tr><tr><td>tn</td><td>▁▇█▆▆▇▇▇▇▇▇▇█▇█▇█▇█▇▇</td></tr><tr><td>tp</td><td>█▃▁▅▅▅▄▅▃▄▄▄▃▅▃▄▃▄▃▄▄</td></tr><tr><td>train_loss</td><td>█▅▄▆▆▅▃▃▂▆▆▁▆▂▇▁▁▁▃▅▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.35631</td></tr><tr><td>accuracy</td><td>0.84151</td></tr><tr><td>auprc</td><td>0.86156</td></tr><tr><td>auroc</td><td>0.9048</td></tr><tr><td>eval_loss</td><td>0.6497</td></tr><tr><td>fn</td><td>40</td></tr><tr><td>fp</td><td>44</td></tr><tr><td>global_step</td><td>532</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.65632</td></tr><tr><td>tn</td><td>297</td></tr><tr><td>tp</td><td>149</td></tr><tr><td>train_loss</td><td>0.02</td></tr></table><br/></div></div>"
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     "output_type": "stream",
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at kornosk/polibertweet-political-twitter-roberta-mlm and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
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      "INFO:simpletransformers.classification.classification_utils: Saving features into cached file cache_dir/cached_train_bertweet_512_2_2\n",
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      "INFO:simpletransformers.classification.classification_utils: Saving features into cached file cache_dir/cached_dev_bertweet_512_2_2\n",
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      "INFO:simpletransformers.classification.classification_utils: Saving features into cached file cache_dir/cached_dev_bertweet_512_2_2\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▇▄▄█▆█▄▅▂▄▂▂▁</td></tr><tr><td>accuracy</td><td>▆▂▁▁▁▁▁▁▁▁▁▂▃▄▅▅▅▆▇▇▇██████</td></tr><tr><td>auprc</td><td>▁▂▃▄▅▅▆▆▇▇▇▇███████████████</td></tr><tr><td>auroc</td><td>▁▃▄▅▅▅▆▆▇▇▇▇▇██████████████</td></tr><tr><td>eval_loss</td><td>█▇▇▆▆▆▆▅▅▅▅▄▄▃▃▃▃▂▂▂▂▁▁▁▁▁▁</td></tr><tr><td>fn</td><td>█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂▂▂▂▂▂▂▂</td></tr><tr><td>fp</td><td>▁▇█████████▇▆▆▅▅▄▃▃▃▂▂▂▂▂▂▂</td></tr><tr><td>global_step</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇████</td></tr><tr><td>lr</td><td>█▇▇▆▆▅▄▄▃▃▂▂▁</td></tr><tr><td>mcc</td><td>▃▂▂▂▂▁▁▁▂▂▃▃▄▅▆▆▆▇▇▇███████</td></tr><tr><td>tn</td><td>█▂▁▁▁▁▁▁▁▁▁▂▃▃▄▄▅▆▆▆▇▇▇▇▇▇▇</td></tr><tr><td>tp</td><td>▁█████████████████▇▇▇▇▇▇▇▇▇</td></tr><tr><td>train_loss</td><td>▇▅█▆▆▃▆▅▇▃▆█▃▆▂▄▃▁▅▃▃▂▃▁▅▄▂</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.46919</td></tr><tr><td>accuracy</td><td>0.67547</td></tr><tr><td>auprc</td><td>0.83718</td></tr><tr><td>auroc</td><td>0.89501</td></tr><tr><td>eval_loss</td><td>0.52131</td></tr><tr><td>fn</td><td>13</td></tr><tr><td>fp</td><td>159</td></tr><tr><td>global_step</td><td>665</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.46181</td></tr><tr><td>tn</td><td>182</td></tr><tr><td>tp</td><td>176</td></tr><tr><td>train_loss</td><td>0.53121</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">elated-sweep-5</strong> at: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/ti0i40z6' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/runs/ti0i40z6</a><br/>Synced 3 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 0m5hfwgz with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 4.977932250565628e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 3\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/0m5hfwgz' target=\"_blank\">peach-sweep-6</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at kornosk/polibertweet-political-twitter-roberta-mlm and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▄▃▂▄█▁▅</td></tr><tr><td>accuracy</td><td>▁▄▅▆▄▄▅▆▇▆▇██▆▆▆</td></tr><tr><td>auprc</td><td>▁▃▃▆▃▇▇█▆▇▇▇▇▇▇▇</td></tr><tr><td>auroc</td><td>▁▃▃▆▂▇██▅▇▇▇▆▆▇▇</td></tr><tr><td>eval_loss</td><td>▅▃▄▁▃▁▃▃█▃▃▇██▇▇</td></tr><tr><td>fn</td><td>▂▅█▃▅▁▃▄▆▂▂▅▅▅▄▄</td></tr><tr><td>fp</td><td>█▄▁▅▄▇▅▃▁▅▄▁▁▃▃▃</td></tr><tr><td>global_step</td><td>▁▁▂▂▂▃▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇██</td></tr><tr><td>lr</td><td>█▇▆▅▃▂▁</td></tr><tr><td>mcc</td><td>▁▄▄▆▃▅▅▆▆▆███▆▆▆</td></tr><tr><td>tn</td><td>▁▅█▄▅▂▄▆█▄▅██▆▆▆</td></tr><tr><td>tp</td><td>▇▅▁▆▄█▆▅▃▇▇▄▄▄▅▅</td></tr><tr><td>train_loss</td><td>▇▅▄▇▆▄▃▂▂▂▅▁▄▁█▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.63293</td></tr><tr><td>accuracy</td><td>0.84717</td></tr><tr><td>auprc</td><td>0.8636</td></tr><tr><td>auroc</td><td>0.90932</td></tr><tr><td>eval_loss</td><td>0.55303</td></tr><tr><td>fn</td><td>41</td></tr><tr><td>fp</td><td>40</td></tr><tr><td>global_step</td><td>399</td></tr><tr><td>lr</td><td>1e-05</td></tr><tr><td>mcc</td><td>0.66656</td></tr><tr><td>tn</td><td>301</td></tr><tr><td>tp</td><td>148</td></tr><tr><td>train_loss</td><td>0.02028</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: n8i0g93j with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 2\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/n8i0g93j' target=\"_blank\">fresh-sweep-7</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un</a>"
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at kornosk/polibertweet-political-twitter-roberta-mlm and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▅▄▁▅█</td></tr><tr><td>accuracy</td><td>▁▄▅▅▅▅▅███</td></tr><tr><td>auprc</td><td>▁▂▂▅▆▇████</td></tr><tr><td>auroc</td><td>▁▂▂▆▅▆▇██▇</td></tr><tr><td>eval_loss</td><td>█▅▇▁▂▂▄▂▄▅</td></tr><tr><td>fn</td><td>▃▅█▁▄▁▂▃▃▄</td></tr><tr><td>fp</td><td>█▄▁▆▄▆▅▃▃▂</td></tr><tr><td>global_step</td><td>▁▂▂▃▃▄▄▅▅▅▆▆▇██</td></tr><tr><td>lr</td><td>█▆▄▃▁</td></tr><tr><td>mcc</td><td>▁▃▄▆▅▆▆███</td></tr><tr><td>tn</td><td>▁▅█▃▅▃▄▆▆▇</td></tr><tr><td>tp</td><td>▆▅▁█▅█▇▆▆▅</td></tr><tr><td>train_loss</td><td>█▆▄█▆▃▂▃▁▂</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.77619</td></tr><tr><td>accuracy</td><td>0.85094</td></tr><tr><td>auprc</td><td>0.87353</td></tr><tr><td>auroc</td><td>0.91639</td></tr><tr><td>eval_loss</td><td>0.44541</td></tr><tr><td>fn</td><td>41</td></tr><tr><td>fp</td><td>38</td></tr><tr><td>global_step</td><td>266</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.67407</td></tr><tr><td>tn</td><td>303</td></tr><tr><td>tp</td><td>148</td></tr><tr><td>train_loss</td><td>0.17115</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: chapubhn with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 2\n",
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/chapubhn' target=\"_blank\">genial-sweep-8</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un</a>"
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at kornosk/polibertweet-political-twitter-roberta-mlm and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▃▅▁▄█</td></tr><tr><td>accuracy</td><td>▁██▇▇█████</td></tr><tr><td>auprc</td><td>▁▃▅▆▅▇▆█▇█</td></tr><tr><td>auroc</td><td>▁▄▅▇▆▆▇▇█▇</td></tr><tr><td>eval_loss</td><td>█▂▃▂▂▁▂▃▂▄</td></tr><tr><td>fn</td><td>▁▇█▄▇▆▅▇▅▇</td></tr><tr><td>fp</td><td>█▁▁▃▁▁▂▁▂▁</td></tr><tr><td>global_step</td><td>▁▂▂▃▃▄▄▅▅▅▆▆▇██</td></tr><tr><td>lr</td><td>█▆▄▃▁</td></tr><tr><td>mcc</td><td>▁▇▇▇▇█████</td></tr><tr><td>tn</td><td>▁██▇▇█▇█▇█</td></tr><tr><td>tp</td><td>█▂▁▅▂▃▄▂▄▂</td></tr><tr><td>train_loss</td><td>▆▃▃▅▄▃▃▁▁█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.90422</td></tr><tr><td>accuracy</td><td>0.85283</td></tr><tr><td>auprc</td><td>0.86723</td></tr><tr><td>auroc</td><td>0.91323</td></tr><tr><td>eval_loss</td><td>0.44628</td></tr><tr><td>fn</td><td>44</td></tr><tr><td>fp</td><td>34</td></tr><tr><td>global_step</td><td>266</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.67604</td></tr><tr><td>tn</td><td>307</td></tr><tr><td>tp</td><td>145</td></tr><tr><td>train_loss</td><td>0.79914</td></tr></table><br/></div></div>"
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       " View project at <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2</a>"
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       " View sweep at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un</a>"
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      "text/html": [
       " View run at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/9vbkszw6' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/runs/9vbkszw6</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at kornosk/polibertweet-political-twitter-roberta-mlm and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▃▃▁▅█</td></tr><tr><td>accuracy</td><td>▁▆▆▅▆▇▇▇▇█</td></tr><tr><td>auprc</td><td>▁▃▃▅▅▆▇██▇</td></tr><tr><td>auroc</td><td>▁▄▄▆▅▆▆██▇</td></tr><tr><td>eval_loss</td><td>█▃▄▁▂▁▆▄▅▆</td></tr><tr><td>fn</td><td>▁▅█▃▆▅▆▆▅▆</td></tr><tr><td>fp</td><td>█▂▂▄▂▃▂▂▃▁</td></tr><tr><td>global_step</td><td>▁▂▂▃▃▄▄▅▅▅▆▆▇██</td></tr><tr><td>lr</td><td>█▆▅▃▁</td></tr><tr><td>mcc</td><td>▁▆▅▅▅▆▆▇▆█</td></tr><tr><td>tn</td><td>▁▇▇▅▇▆▇▇▆█</td></tr><tr><td>tp</td><td>█▃▁▅▃▅▃▃▄▃</td></tr><tr><td>train_loss</td><td>▇▅▃▆▃▃▃▂▁█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.859</td></tr><tr><td>accuracy</td><td>0.85849</td></tr><tr><td>auprc</td><td>0.87063</td></tr><tr><td>auroc</td><td>0.90875</td></tr><tr><td>eval_loss</td><td>0.48842</td></tr><tr><td>fn</td><td>45</td></tr><tr><td>fp</td><td>30</td></tr><tr><td>global_step</td><td>266</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.68742</td></tr><tr><td>tn</td><td>311</td></tr><tr><td>tp</td><td>144</td></tr><tr><td>train_loss</td><td>0.71393</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: zkutvblv with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 2\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231103_231129-zkutvblv</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/zkutvblv' target=\"_blank\">crimson-sweep-10</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/eo1fu2un</a>"
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at kornosk/polibertweet-political-twitter-roberta-mlm and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▃▃▁▅█</td></tr><tr><td>accuracy</td><td>▁▆▆▆▇▇▇▇▇█</td></tr><tr><td>auprc</td><td>▁▃▃▅▅▆▇███</td></tr><tr><td>auroc</td><td>▁▄▄▆▅▆▇██▇</td></tr><tr><td>eval_loss</td><td>█▂▅▁▂▁▄▃▅▆</td></tr><tr><td>fn</td><td>▁▅█▄▅▄▅▅▅▆</td></tr><tr><td>fp</td><td>█▃▁▃▂▃▂▂▂▁</td></tr><tr><td>global_step</td><td>▁▂▂▃▃▄▄▅▅▅▆▆▇██</td></tr><tr><td>lr</td><td>█▆▅▃▁</td></tr><tr><td>mcc</td><td>▁▆▅▅▆▆▆▇▇█</td></tr><tr><td>tn</td><td>▁▆█▆▇▆▇▇▇█</td></tr><tr><td>tp</td><td>█▄▁▅▄▅▄▄▄▃</td></tr><tr><td>train_loss</td><td>▇▄▃▆▃▃▃▁▁█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.92007</td></tr><tr><td>accuracy</td><td>0.85849</td></tr><tr><td>auprc</td><td>0.8669</td></tr><tr><td>auroc</td><td>0.90575</td></tr><tr><td>eval_loss</td><td>0.49237</td></tr><tr><td>fn</td><td>46</td></tr><tr><td>fp</td><td>29</td></tr><tr><td>global_step</td><td>266</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.68706</td></tr><tr><td>tn</td><td>312</td></tr><tr><td>tp</td><td>143</td></tr><tr><td>train_loss</td><td>0.71115</td></tr></table><br/></div></div>"
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       "Find logs at: <code>.\\wandb\\run-20231103_231129-zkutvblv\\logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "output_type": "display_data"
    }
   ],
   "source": [
    "sweep_config = {\n",
    "    \"method\": \"bayes\",\n",
    "    \"metric\": {\"name\": \"mcc\", \"goal\": \"maximize\"},\n",
    "    \"parameters\": {\n",
    "        \"num_train_epochs\": {\"min\": 2, \"max\": 5},\n",
    "        \"learning_rate\": {\"min\": 0.0, \"max\": 5e-05 },\n",
    "    },\n",
    "}\n",
    "\n",
    "sweep_id = wandb.sweep(sweep_config, project=\"trump-BERTweet2\")\n",
    "\n",
    "# logging\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "transformers_logger = logging.getLogger(\"transformers\")\n",
    "transformers_logger.setLevel(logging.WARNING)\n",
    "\n",
    "# args\n",
    "model_args = ClassificationArgs()\n",
    "model_args.evaluate_during_training = True\n",
    "model_args.evaluate_during_training_silent = False\n",
    "model_args.evaluate_during_training_steps = 30\n",
    "model_args.manual_seed = 1\n",
    "model_args.max_seq_length = 512\n",
    "model_args.save_eval_checkpoints = False\n",
    "model_args.save_model_every_epoch = True\n",
    "model_args.overwrite_output_dir = True\n",
    "model_args.reprocess_input_data = True\n",
    "model_args.train_batch_size = 16\n",
    "model_args.eval_batch_size = 16\n",
    "model_args.train_custom_parameters_only = False\n",
    "model_args.wandb_project = \"trump-BERTweet2\"\n",
    "\n",
    "# training function\n",
    "def train():\n",
    "    # Initialize a new wandb run\n",
    "    wandb.init(resume = True)\n",
    "    # Create a TransformerModel\n",
    "    model = ClassificationModel(\n",
    "        \"bertweet\",\n",
    "        \"kornosk/polibertweet-political-twitter-roberta-mlm\",\n",
    "        weight = [1,3],\n",
    "        use_cuda=True,\n",
    "        args=model_args,\n",
    "        sweep_config=wandb.config,\n",
    "    )\n",
    "\n",
    "    # Train the model\n",
    "    model.train_model(\n",
    "        train_set,\n",
    "        eval_df=val_set,\n",
    "        verbose = False,\n",
    "        accuracy=lambda truth, predictions: accuracy_score(\n",
    "            truth, [round(p) for p in predictions]\n",
    "        ),\n",
    "    )\n",
    "    # Sync wandb\n",
    "    wandb.join()\n",
    "\n",
    "# train\n",
    "wandb.agent(sweep_id, train, count = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba96ae6d-11eb-4c7a-b16a-9878629c2353",
   "metadata": {},
   "source": [
    "## RoBERTa Sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "055b3097-9155-4505-b2fd-8deaedcfabb0",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create sweep with ID: csn6082t\n",
      "Sweep URL: https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: u6pdfqs3 with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 3.5452280133480105e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 4\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.12"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231103_231935-u6pdfqs3</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/u6pdfqs3' target=\"_blank\">soft-sweep-1</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View sweep at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/u6pdfqs3' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/runs/u6pdfqs3</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading (…)lve/main/config.json: 100%|████████████| 481/481 [00:00<?, ?B/s]\n",
      "C:\\Users\\mikeb\\.conda\\envs\\sandbox\\Lib\\site-packages\\huggingface_hub\\file_download.py:137: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\mikeb\\.cache\\huggingface\\hub. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n",
      "Downloading model.safetensors: 100%|████████| 499M/499M [00:43<00:00, 11.5MB/s]\n",
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "Downloading (…)olve/main/vocab.json: 100%|██| 899k/899k [00:00<00:00, 4.48MB/s]\n",
      "Downloading (…)olve/main/merges.txt: 100%|██| 456k/456k [00:00<00:00, 9.95MB/s]\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>█▇▇▇▃▂▃▂▂▁</td></tr><tr><td>accuracy</td><td>▁▇▇▇▆███████▆█▇▇████▇</td></tr><tr><td>auprc</td><td>▁▂▆▆▆▆▆▇▇█▇▇█████████</td></tr><tr><td>auroc</td><td>▁▂▅▆▆▇▇▇▇█▇▇█▇▇▇▇▇▇▇▇</td></tr><tr><td>eval_loss</td><td>▆▃▂▂▂▂▁▁▃▁▂▃▂▅▄▃██▇██</td></tr><tr><td>fn</td><td>▁█▆▅▄▇▆▆▇▇▇▇▄▇▇▆█▇▇▇▇</td></tr><tr><td>fp</td><td>█▂▂▃▃▁▂▂▁▂▁▁▃▁▂▂▁▁▁▁▂</td></tr><tr><td>global_step</td><td>▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇███</td></tr><tr><td>lr</td><td>█▇▆▆▅▄▃▃▂▁</td></tr><tr><td>mcc</td><td>▁▆▇▇▆▇▇▇█▇██▆█▇▇██▇▇▇</td></tr><tr><td>tn</td><td>▁▇▇▆▆█▇▇█▇██▆█▇▇████▇</td></tr><tr><td>tp</td><td>█▁▃▄▅▂▃▃▂▂▂▂▅▂▂▃▁▂▂▂▂</td></tr><tr><td>train_loss</td><td>▆▇▄▃▅▅▄▅▄▃▁▁▃▃█▂▄▁▆▃▂</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.04437</td></tr><tr><td>accuracy</td><td>0.79245</td></tr><tr><td>auprc</td><td>0.82109</td></tr><tr><td>auroc</td><td>0.85689</td></tr><tr><td>eval_loss</td><td>0.69672</td></tr><tr><td>fn</td><td>56</td></tr><tr><td>fp</td><td>54</td></tr><tr><td>global_step</td><td>532</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.54665</td></tr><tr><td>tn</td><td>287</td></tr><tr><td>tp</td><td>133</td></tr><tr><td>train_loss</td><td>0.26273</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: eh516y1x with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 4.846864990273883e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 4\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231103_232551-eh516y1x</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/eh516y1x' target=\"_blank\">serene-sweep-2</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
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       " View project at <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>█▆▆▆▂▁▄▅▁▁</td></tr><tr><td>accuracy</td><td>▁▇▇▇▇▇▇▆█▇█▇▆▇▇▇█████</td></tr><tr><td>auprc</td><td>▁▃▂▄▆▄▅▄▇▇██▆▇██▇▇███</td></tr><tr><td>auroc</td><td>▁▄▄▅▇▄▆▅▇▇▇█▇▇▇█▇▇███</td></tr><tr><td>eval_loss</td><td>▇▄▃▃▂▂▁▂▂▁▁▃▅▃▃▂▇█▇█▇</td></tr><tr><td>fn</td><td>▁▆▇▆▆█▇▅▇▆▇▆▃▆▇▆██▆▇▆</td></tr><tr><td>fp</td><td>█▂▂▂▂▂▂▃▁▂▁▂▄▂▂▂▁▁▁▁▂</td></tr><tr><td>global_step</td><td>▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇███</td></tr><tr><td>lr</td><td>█▇▆▆▅▄▃▃▂▁</td></tr><tr><td>mcc</td><td>▁▆▆▇▇▇▇▆█▇▇▇▆▇▇▇▇▇██▇</td></tr><tr><td>tn</td><td>▁▇▇▇▇▇▇▆█▇█▇▅▇▇▇████▇</td></tr><tr><td>tp</td><td>█▃▂▃▃▁▂▄▂▃▂▃▆▃▂▃▁▁▃▂▃</td></tr><tr><td>train_loss</td><td>▆▅▄▄▅▄▅▆▃▄▂▁▅▅█▂▆▁▇▃▅</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.09403</td></tr><tr><td>accuracy</td><td>0.81698</td></tr><tr><td>auprc</td><td>0.83151</td></tr><tr><td>auroc</td><td>0.87015</td></tr><tr><td>eval_loss</td><td>0.63146</td></tr><tr><td>fn</td><td>49</td></tr><tr><td>fp</td><td>48</td></tr><tr><td>global_step</td><td>532</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.60069</td></tr><tr><td>tn</td><td>293</td></tr><tr><td>tp</td><td>140</td></tr><tr><td>train_loss</td><td>0.56757</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: l14ztoox with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 4.5509779966939783e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 4\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231103_233122-l14ztoox</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/l14ztoox' target=\"_blank\">vivid-sweep-3</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
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       " View project at <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2</a>"
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       " View sweep at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
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       " View run at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/l14ztoox' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/runs/l14ztoox</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
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      "INFO:simpletransformers.classification.classification_utils: Saving features into cached file cache_dir/cached_train_roberta_512_2_2\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>█▇▆▆▃▂▂▄▁▁</td></tr><tr><td>accuracy</td><td>▁▆▇▇▇██▇██▇█▇████████</td></tr><tr><td>auprc</td><td>▁▂▅▆▆▄▇▆▇▇▇█▆▇▇▇█████</td></tr><tr><td>auroc</td><td>▁▂▃▄▃▂▆▅▅▆▆▇▇█▇▇▇▇██▇</td></tr><tr><td>eval_loss</td><td>█▃▂▃▂▃▁▂▁▃▂▆▂▅▃▁▇▇▇▆▇</td></tr><tr><td>fn</td><td>▁▅▇▅▆█▆▅▇█▇▇▄▆▇▆▇▆▆▆▅</td></tr><tr><td>fp</td><td>█▃▂▂▂▁▂▂▁▁▁▁▃▁▁▂▁▁▁▂▂</td></tr><tr><td>global_step</td><td>▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇███</td></tr><tr><td>lr</td><td>█▇▆▆▅▄▃▃▂▁</td></tr><tr><td>mcc</td><td>▁▆▇▇▇▇▇▇█▇▇█▆██▇███▇█</td></tr><tr><td>tn</td><td>▁▆▇▇▇█▇▇████▆██▇███▇▇</td></tr><tr><td>tp</td><td>█▄▂▄▃▁▃▄▃▁▂▂▅▃▂▃▂▃▃▃▃</td></tr><tr><td>train_loss</td><td>▅▆▄▃▄▄▄▆▃▃▂▁▃▄█▂▆▁▆▂▂</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.09597</td></tr><tr><td>accuracy</td><td>0.80377</td></tr><tr><td>auprc</td><td>0.82915</td></tr><tr><td>auroc</td><td>0.8646</td></tr><tr><td>eval_loss</td><td>0.57887</td></tr><tr><td>fn</td><td>47</td></tr><tr><td>fp</td><td>57</td></tr><tr><td>global_step</td><td>532</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.57784</td></tr><tr><td>tn</td><td>284</td></tr><tr><td>tp</td><td>142</td></tr><tr><td>train_loss</td><td>0.17113</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: zg3mj7ls with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 4.792142257908055e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 4\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231103_233638-zg3mj7ls</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/zg3mj7ls' target=\"_blank\">volcanic-sweep-4</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
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       " View project at <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▁▂█</td></tr><tr><td>accuracy</td><td>▁█▁▁▆█</td></tr><tr><td>auprc</td><td>▆▇▁▇█▇</td></tr><tr><td>auroc</td><td>▆▇▁▇█▇</td></tr><tr><td>eval_loss</td><td>█▁█▆▂▇</td></tr><tr><td>fn</td><td>▁▅▁▁▃█</td></tr><tr><td>fp</td><td>█▂██▄▁</td></tr><tr><td>global_step</td><td>▁▂▃▅▅▆▇██</td></tr><tr><td>lr</td><td>█▅▁</td></tr><tr><td>mcc</td><td>▁█▁▂▆█</td></tr><tr><td>tn</td><td>▁▇▁▁▅█</td></tr><tr><td>tp</td><td>█▄██▆▁</td></tr><tr><td>train_loss</td><td>▅▄▃▂▁█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.79318</td></tr><tr><td>accuracy</td><td>0.79434</td></tr><tr><td>auprc</td><td>0.75836</td></tr><tr><td>auroc</td><td>0.80731</td></tr><tr><td>eval_loss</td><td>0.60605</td></tr><tr><td>fn</td><td>89</td></tr><tr><td>fp</td><td>20</td></tr><tr><td>global_step</td><td>150</td></tr><tr><td>lr</td><td>4e-05</td></tr><tr><td>mcc</td><td>0.53844</td></tr><tr><td>tn</td><td>321</td></tr><tr><td>tp</td><td>100</td></tr><tr><td>train_loss</td><td>0.79318</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">volcanic-sweep-4</strong> at: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/zg3mj7ls' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/runs/zg3mj7ls</a><br/>Synced 3 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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      "Run zg3mj7ls errored: PermissionError(13, 'Permission denied')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run zg3mj7ls errored: PermissionError(13, 'Permission denied')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 3i9ykc8n with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 3.726393546451714e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 5\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Tried to auto resume run with id zg3mj7ls but id 3i9ykc8n is set.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231103_233842-3i9ykc8n</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/3i9ykc8n' target=\"_blank\">happy-sweep-5</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
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      "INFO:simpletransformers.classification.classification_utils: Saving features into cached file cache_dir/cached_train_roberta_512_2_2\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>█▂▁</td></tr><tr><td>accuracy</td><td>▁████</td></tr><tr><td>auprc</td><td>▁▅█▇▆</td></tr><tr><td>auroc</td><td>▁▅▇██</td></tr><tr><td>eval_loss</td><td>█▃▁▁▁</td></tr><tr><td>fn</td><td>▁█▇▆▇</td></tr><tr><td>fp</td><td>█▁▂▂▂</td></tr><tr><td>global_step</td><td>▁▂▃▅▅▆▇█</td></tr><tr><td>lr</td><td>█▄▁</td></tr><tr><td>mcc</td><td>▁████</td></tr><tr><td>tn</td><td>▁█▇▇▇</td></tr><tr><td>tp</td><td>█▁▂▃▂</td></tr><tr><td>train_loss</td><td>██▁▂▅</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.44493</td></tr><tr><td>accuracy</td><td>0.76226</td></tr><tr><td>auprc</td><td>0.7702</td></tr><tr><td>auroc</td><td>0.83828</td></tr><tr><td>eval_loss</td><td>0.4784</td></tr><tr><td>fn</td><td>48</td></tr><tr><td>fp</td><td>78</td></tr><tr><td>global_step</td><td>150</td></tr><tr><td>lr</td><td>3e-05</td></tr><tr><td>mcc</td><td>0.5032</td></tr><tr><td>tn</td><td>263</td></tr><tr><td>tp</td><td>141</td></tr><tr><td>train_loss</td><td>0.56034</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: cw1gqnbe with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 2\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Tried to auto resume run with id 3i9ykc8n but id cw1gqnbe is set.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231103_234040-cw1gqnbe</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/cw1gqnbe' target=\"_blank\">good-sweep-6</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>█▁▃</td></tr><tr><td>accuracy</td><td>▁▁▁▂▂▄█</td></tr><tr><td>auprc</td><td>▁▅▆▇▇██</td></tr><tr><td>auroc</td><td>▁▅▆▇▇██</td></tr><tr><td>eval_loss</td><td>█▇▆▃▃▂▁</td></tr><tr><td>fn</td><td>▁▁▁▁▁▂█</td></tr><tr><td>fp</td><td>█████▆▁</td></tr><tr><td>global_step</td><td>▁▂▂▄▄▅▆▇▇█</td></tr><tr><td>lr</td><td>█▅▁</td></tr><tr><td>mcc</td><td>▁▁▁▃▃▅█</td></tr><tr><td>tn</td><td>▁▁▁▁▁▃█</td></tr><tr><td>tp</td><td>█████▇▁</td></tr><tr><td>train_loss</td><td>█▃▅▂▁▅▂</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.61562</td></tr><tr><td>accuracy</td><td>0.63774</td></tr><tr><td>auprc</td><td>0.7705</td></tr><tr><td>auroc</td><td>0.83326</td></tr><tr><td>eval_loss</td><td>0.50369</td></tr><tr><td>fn</td><td>24</td></tr><tr><td>fp</td><td>168</td></tr><tr><td>global_step</td><td>180</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.37699</td></tr><tr><td>tn</td><td>173</td></tr><tr><td>tp</td><td>165</td></tr><tr><td>train_loss</td><td>0.54722</td></tr></table><br/></div></div>"
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      "Run cw1gqnbe errored: RuntimeError('File outputs/best_model\\\\scheduler.pt cannot be opened.')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run cw1gqnbe errored: RuntimeError('File outputs/best_model\\\\scheduler.pt cannot be opened.')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: krvvltqj with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 6.222264479223999e-06\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 3\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Tried to auto resume run with id cw1gqnbe but id krvvltqj is set.\n"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/krvvltqj' target=\"_blank\">deep-sweep-7</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>█▇▇▄▂▁▄</td></tr><tr><td>accuracy</td><td>▁▁▁▁▁▁▅▆█▇██▇███</td></tr><tr><td>auprc</td><td>▁▅▆▇▇▇▇█████████</td></tr><tr><td>auroc</td><td>▁▅▆▇▇▇▇█████████</td></tr><tr><td>eval_loss</td><td>█▇▆▄▄▄▃▃▂▂▂▂▂▁▁▁</td></tr><tr><td>fn</td><td>▁▁▁▁▁▁▄▅█▇▇▇▇▇██</td></tr><tr><td>fp</td><td>██████▅▃▁▂▁▁▂▁▁▁</td></tr><tr><td>global_step</td><td>▁▁▂▂▂▃▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇██</td></tr><tr><td>lr</td><td>█▇▆▅▃▂▁</td></tr><tr><td>mcc</td><td>▁▁▁▁▁▃▅▆█▇██▇███</td></tr><tr><td>tn</td><td>▁▁▁▁▁▁▄▆█▇██▇███</td></tr><tr><td>tp</td><td>██████▅▄▁▂▂▂▂▂▁▁</td></tr><tr><td>train_loss</td><td>▆▅▅▅▄▅▄▅▂▃▂▁▄▃█▃</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.46407</td></tr><tr><td>accuracy</td><td>0.77736</td></tr><tr><td>auprc</td><td>0.79597</td></tr><tr><td>auroc</td><td>0.8408</td></tr><tr><td>eval_loss</td><td>0.46321</td></tr><tr><td>fn</td><td>48</td></tr><tr><td>fp</td><td>70</td></tr><tr><td>global_step</td><td>399</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.52914</td></tr><tr><td>tn</td><td>271</td></tr><tr><td>tp</td><td>141</td></tr><tr><td>train_loss</td><td>0.46121</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 413liu8m with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 4.618905740625056e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 3\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/413liu8m' target=\"_blank\">serene-sweep-8</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
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     ]
    },
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     "output_type": "stream",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>█▇▅▇▃▁▃</td></tr><tr><td>accuracy</td><td>▁▇▇▇▇▇█▇████▇█▇▇</td></tr><tr><td>auprc</td><td>▁▂▃▅▆▁▅▄▇▇▇█▇███</td></tr><tr><td>auroc</td><td>▁▃▂▆▆▃▅▄███▇▇▇██</td></tr><tr><td>eval_loss</td><td>█▅▄▂▂▄▂▃▂▁▂▄▃▆▄▄</td></tr><tr><td>fn</td><td>▁██▅▅▅▆▄▆▅▇▆▄▆▅▅</td></tr><tr><td>fp</td><td>█▁▁▂▂▂▂▃▁▁▁▁▂▁▂▂</td></tr><tr><td>global_step</td><td>▁▁▂▂▂▃▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇██</td></tr><tr><td>lr</td><td>█▇▆▄▃▂▁</td></tr><tr><td>mcc</td><td>▁▇▇▇▇▆▇▆████▇█▇▇</td></tr><tr><td>tn</td><td>▁██▇▇▇▇▆████▇█▇▇</td></tr><tr><td>tp</td><td>█▁▁▄▅▄▃▅▃▄▂▃▅▃▄▄</td></tr><tr><td>train_loss</td><td>▅▅▄▃▄▃▄▅▂▃▂▁▂▄█▂</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.20835</td></tr><tr><td>accuracy</td><td>0.8</td></tr><tr><td>auprc</td><td>0.82619</td></tr><tr><td>auroc</td><td>0.86838</td></tr><tr><td>eval_loss</td><td>0.52221</td></tr><tr><td>fn</td><td>48</td></tr><tr><td>fp</td><td>58</td></tr><tr><td>global_step</td><td>399</td></tr><tr><td>lr</td><td>1e-05</td></tr><tr><td>mcc</td><td>0.5697</td></tr><tr><td>tn</td><td>283</td></tr><tr><td>tp</td><td>141</td></tr><tr><td>train_loss</td><td>0.22179</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 5hoqvzhq with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 9.213834575839936e-06\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 5\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231103_235438-5hoqvzhq</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/5hoqvzhq' target=\"_blank\">wobbly-sweep-9</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>█▇▆▅▃▂▄▃▁▁▁▃▄</td></tr><tr><td>accuracy</td><td>▁▁▂▆▄▇█▇▇███▇█▇▇███████████</td></tr><tr><td>auprc</td><td>▁▅▆▆▆▇▇▇▇█▇████████████████</td></tr><tr><td>auroc</td><td>▁▅▆▇▇▇▇▇▇▇▇███▇████████████</td></tr><tr><td>eval_loss</td><td>█▇▄▃▄▂▂▁▂▁▁▂▁▁▂▁▂▁▂▁▂▂▂▂▂▂▂</td></tr><tr><td>fn</td><td>▁▁▂▄▃▄▆▅▅▆██▅▆▆▆▇▆▆▅▇▇▆▆▆▆▆</td></tr><tr><td>fp</td><td>██▇▃▅▃▂▂▂▁▁▁▂▁▂▂▁▂▁▂▁▁▁▁▂▁▁</td></tr><tr><td>global_step</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇████</td></tr><tr><td>lr</td><td>█▇▇▆▆▅▄▄▃▃▂▂▁</td></tr><tr><td>mcc</td><td>▁▁▃▆▅▇▇▇▇▇▇▇▇█▇▇███████████</td></tr><tr><td>tn</td><td>▁▁▂▆▄▆▇▇▇███▇█▇▇█▇█▇████▇██</td></tr><tr><td>tp</td><td>██▇▅▆▅▃▄▄▃▁▁▄▃▃▃▂▃▃▄▂▂▃▃▃▃▃</td></tr><tr><td>train_loss</td><td>▆▅▅▄▄▄▄▅▃▃▂▂▄▃█▄▇▁▆▃▂▃▁▂▂▃▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.35189</td></tr><tr><td>accuracy</td><td>0.80566</td></tr><tr><td>auprc</td><td>0.83138</td></tr><tr><td>auroc</td><td>0.87129</td></tr><tr><td>eval_loss</td><td>0.48156</td></tr><tr><td>fn</td><td>47</td></tr><tr><td>fp</td><td>56</td></tr><tr><td>global_step</td><td>665</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.58132</td></tr><tr><td>tn</td><td>285</td></tr><tr><td>tp</td><td>142</td></tr><tr><td>train_loss</td><td>0.12777</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 6uwk9mgr with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 1.4561958429078752e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 4\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/6uwk9mgr' target=\"_blank\">clear-sweep-10</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/csn6082t</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
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      "INFO:simpletransformers.classification.classification_utils: Saving features into cached file cache_dir/cached_dev_roberta_512_2_2\n",
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      "Epoch 4 of 4: 100%|██████████████████████████████| 4/4 [05:17<00:00, 79.41s/it]\n"
     ]
    },
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     "data": {
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       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>█▇▆▆▂▂▄▃▁▁</td></tr><tr><td>accuracy</td><td>▁▁▆█▆▇█▇████▇█▇██████</td></tr><tr><td>auprc</td><td>▁▄▆▇▆▆▇▇█████████████</td></tr><tr><td>auroc</td><td>▁▅▆▇▇▇▇▇█▇▇██████████</td></tr><tr><td>eval_loss</td><td>█▅▃▂▃▂▁▁▁▁▂▃▁▂▂▂▃▃▃▃▃</td></tr><tr><td>fn</td><td>▁▁▄▇▄▅▆▅▇▆██▅▇▅▇▇▆▆▆▆</td></tr><tr><td>fp</td><td>██▄▂▄▂▂▂▁▂▁▁▂▁▂▁▁▁▂▂▂</td></tr><tr><td>global_step</td><td>▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇███</td></tr><tr><td>lr</td><td>█▇▆▆▅▄▃▃▂▁</td></tr><tr><td>mcc</td><td>▁▁▆▇▆▇█▇█▇████▇██████</td></tr><tr><td>tn</td><td>▁▁▅▇▅▇▇▇█▇██▇█▇███▇▇▇</td></tr><tr><td>tp</td><td>██▅▂▅▄▃▄▂▃▁▁▄▂▄▂▂▃▃▃▃</td></tr><tr><td>train_loss</td><td>▆▅▄▄▄▄▄▅▃▃▂▂▃▃█▃▆▁▆▃▂</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.08482</td></tr><tr><td>accuracy</td><td>0.8</td></tr><tr><td>auprc</td><td>0.83269</td></tr><tr><td>auroc</td><td>0.86751</td></tr><tr><td>eval_loss</td><td>0.5143</td></tr><tr><td>fn</td><td>46</td></tr><tr><td>fp</td><td>60</td></tr><tr><td>global_step</td><td>532</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.57215</td></tr><tr><td>tn</td><td>281</td></tr><tr><td>tp</td><td>143</td></tr><tr><td>train_loss</td><td>0.19758</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">clear-sweep-10</strong> at: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/6uwk9mgr' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/runs/6uwk9mgr</a><br/>Synced 3 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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   "source": [
    "sweep_config = {\n",
    "    \"method\": \"bayes\",\n",
    "    \"metric\": {\"name\": \"mcc\", \"goal\": \"maximize\"},\n",
    "    \"parameters\": {\n",
    "        \"num_train_epochs\": {\"min\": 2, \"max\": 5},\n",
    "        \"learning_rate\": {\"min\": 0.0, \"max\": 5e-05 },\n",
    "    },\n",
    "}\n",
    "\n",
    "sweep_id = wandb.sweep(sweep_config, project=\"trump-BERTweet2\")\n",
    "\n",
    "# logging\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "transformers_logger = logging.getLogger(\"transformers\")\n",
    "transformers_logger.setLevel(logging.WARNING)\n",
    "\n",
    "# args\n",
    "model_args = ClassificationArgs()\n",
    "model_args.evaluate_during_training = True\n",
    "model_args.evaluate_during_training_silent = False\n",
    "model_args.evaluate_during_training_steps = 30\n",
    "model_args.manual_seed = 1\n",
    "model_args.max_seq_length = 512\n",
    "model_args.save_eval_checkpoints = False\n",
    "model_args.save_model_every_epoch = True\n",
    "model_args.overwrite_output_dir = True\n",
    "model_args.reprocess_input_data = True\n",
    "model_args.train_batch_size = 16\n",
    "model_args.eval_batch_size = 16\n",
    "model_args.train_custom_parameters_only = False\n",
    "model_args.wandb_project = \"trump-BERTweet2\"\n",
    "\n",
    "# training function\n",
    "def train():\n",
    "    # Initialize a new wandb run\n",
    "    wandb.init(resume = True)\n",
    "\n",
    "    # Create a TransformerModel\n",
    "    model = ClassificationModel(\n",
    "        \"roberta\",\n",
    "        \"roberta-base\",\n",
    "        weight = [1,3],\n",
    "        use_cuda=True,\n",
    "        args=model_args,\n",
    "        sweep_config=wandb.config,\n",
    "    )\n",
    "\n",
    "    # Train the model\n",
    "    model.train_model(\n",
    "        train_set,\n",
    "        eval_df=val_set,\n",
    "        verbose = False,\n",
    "        accuracy=lambda truth, predictions: accuracy_score(\n",
    "            truth, [round(p) for p in predictions]\n",
    "        ),\n",
    "    )\n",
    "    # Sync wandb\n",
    "    wandb.join()\n",
    "\n",
    "# train\n",
    "wandb.agent(sweep_id, train, count = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e181aaa2-22a6-429c-b649-a077639457d8",
   "metadata": {},
   "source": [
    "## Bertweet Sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "11c794e6-5a56-48a0-a849-b406d698eb5a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "INFO:wandb.agents.pyagent:Starting sweep agent: entity=None, project=None, count=10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create sweep with ID: 982tyfws\n",
      "Sweep URL: https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ucmspetr with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 4.780975850547418e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 5\n",
      "ERROR:wandb.jupyter:Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmlburnham\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.12"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231104_115328-ucmspetr</code>"
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     "data": {
      "text/html": [
       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/ucmspetr' target=\"_blank\">ethereal-sweep-1</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws</a>"
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     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2</a>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       " View sweep at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws</a>"
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       " View run at <a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/ucmspetr' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/runs/ucmspetr</a>"
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     "text": [
      "Downloading (…)lve/main/config.json: 100%|████████████| 558/558 [00:00<?, ?B/s]\n",
      "Downloading pytorch_model.bin: 100%|████████| 543M/543M [00:47<00:00, 11.5MB/s]\n",
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/bertweet-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "Downloading (…)solve/main/vocab.txt: 100%|██| 843k/843k [00:00<00:00, 4.50MB/s]\n",
      "Downloading (…)solve/main/bpe.codes: 100%|█| 1.08M/1.08M [00:00<00:00, 12.0MB/s\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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      "INFO:simpletransformers.classification.classification_utils: Saving features into cached file cache_dir/cached_train_bertweet_512_2_2\n",
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      "Epochs 3/5. Running Loss:    0.0153:  38%|█▌  | 51/133 [00:25<03:24,  2.49s/it]\u001b[A\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▆▅▃▅█▂▅▂▁▂▁▁▃</td></tr><tr><td>accuracy</td><td>▁▇▇▇▇▇▇████▇█▇█████████████</td></tr><tr><td>auprc</td><td>▁▅▇▇▇██▇████████▇▇▇▆▆▇▆▆▆▆▆</td></tr><tr><td>auroc</td><td>▁▅▇▇▇▇█████▇▇█▇█▇▇▇▇▇▇▇▇▇▇▇</td></tr><tr><td>eval_loss</td><td>▄▂▁▂▁▁▁▁▁▂▂▂▄▂▅▃▆▆▆▅▆████▇▇</td></tr><tr><td>fn</td><td>▁▆▅▇▅▆▄▄▆▆▆▆▇▅█▆▇▆▆▆▇█▇▇▇▆▆</td></tr><tr><td>fp</td><td>█▂▂▂▂▂▂▂▂▁▁▂▁▂▁▂▁▂▂▂▁▁▁▁▁▁▁</td></tr><tr><td>global_step</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇████</td></tr><tr><td>lr</td><td>█▇▇▆▆▅▄▄▃▃▂▂▁</td></tr><tr><td>mcc</td><td>▁▆▇▇▇▇▇█▇██▇█▇▇██▇█▇█▇█████</td></tr><tr><td>tn</td><td>▁▇▇▇▇▇▇▇▇██▇█▇█▇█▇▇▇███████</td></tr><tr><td>tp</td><td>█▃▄▂▄▃▅▅▃▃▃▃▂▄▁▃▂▃▃▃▂▁▂▂▂▃▃</td></tr><tr><td>train_loss</td><td>▇▅▇█▅▃▅▄▄▆▄▂▆▃▆▁▄▁▄▃▁▃▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.22638</td></tr><tr><td>accuracy</td><td>0.82075</td></tr><tr><td>auprc</td><td>0.76035</td></tr><tr><td>auroc</td><td>0.86201</td></tr><tr><td>eval_loss</td><td>0.91386</td></tr><tr><td>fn</td><td>48</td></tr><tr><td>fp</td><td>47</td></tr><tr><td>global_step</td><td>665</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.60893</td></tr><tr><td>tn</td><td>294</td></tr><tr><td>tp</td><td>141</td></tr><tr><td>train_loss</td><td>0.01254</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: zmuoa3m1 with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 2\n",
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/zmuoa3m1' target=\"_blank\">lucky-sweep-2</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws</a>"
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/bertweet-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>█▃▁▅█</td></tr><tr><td>accuracy</td><td>▁▁▇▇█▇▇███</td></tr><tr><td>auprc</td><td>▁▅▆▆▇▇▇███</td></tr><tr><td>auroc</td><td>▁▆▇▇▇█████</td></tr><tr><td>eval_loss</td><td>█▇▄▃▂▂▂▁▁▁</td></tr><tr><td>fn</td><td>▁▁▇▆█▆▅█▇█</td></tr><tr><td>fp</td><td>██▂▂▁▂▃▁▁▁</td></tr><tr><td>global_step</td><td>▁▂▂▃▃▄▄▅▅▅▆▆▇██</td></tr><tr><td>lr</td><td>█▆▅▃▁</td></tr><tr><td>mcc</td><td>▁▁▆▆▇▇▇███</td></tr><tr><td>tn</td><td>▁▁▇▇█▇▇███</td></tr><tr><td>tp</td><td>██▂▃▁▃▄▁▂▁</td></tr><tr><td>train_loss</td><td>▇▅▇█▅▁▄▃▁▃</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.72117</td></tr><tr><td>accuracy</td><td>0.8283</td></tr><tr><td>auprc</td><td>0.82954</td></tr><tr><td>auroc</td><td>0.88024</td></tr><tr><td>eval_loss</td><td>0.42799</td></tr><tr><td>fn</td><td>44</td></tr><tr><td>fp</td><td>47</td></tr><tr><td>global_step</td><td>266</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.62719</td></tr><tr><td>tn</td><td>294</td></tr><tr><td>tp</td><td>145</td></tr><tr><td>train_loss</td><td>0.46414</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: b222k0ky with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 5.13169668273944e-07\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 2\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231104_120420-b222k0ky</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/b222k0ky' target=\"_blank\">glamorous-sweep-3</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/bertweet-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▇▂▁█▂</td></tr><tr><td>accuracy</td><td>█▁▁▁▂▂▂▂▂▂</td></tr><tr><td>auprc</td><td>▁▂▃▅▆▆▇███</td></tr><tr><td>auroc</td><td>▁▂▄▅▆▆▇███</td></tr><tr><td>eval_loss</td><td>█▆▄▃▃▂▁▁▁▁</td></tr><tr><td>fn</td><td>█▃▁▁▁▁▁▁▁▁</td></tr><tr><td>fp</td><td>▁▇████████</td></tr><tr><td>global_step</td><td>▁▂▂▃▃▄▄▅▅▅▆▆▇██</td></tr><tr><td>lr</td><td>█▆▅▃▁</td></tr><tr><td>mcc</td><td>▆▃▁▃██████</td></tr><tr><td>tn</td><td>█▂▁▁▁▁▁▁▁▁</td></tr><tr><td>tp</td><td>▁▆████████</td></tr><tr><td>train_loss</td><td>▆▆█▅▆▂▅▄█▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.65104</td></tr><tr><td>accuracy</td><td>0.3566</td></tr><tr><td>auprc</td><td>0.36709</td></tr><tr><td>auroc</td><td>0.52415</td></tr><tr><td>eval_loss</td><td>0.67428</td></tr><tr><td>fn</td><td>0</td></tr><tr><td>fp</td><td>341</td></tr><tr><td>global_step</td><td>266</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.0</td></tr><tr><td>tn</td><td>0</td></tr><tr><td>tp</td><td>189</td></tr><tr><td>train_loss</td><td>0.62222</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: koa7gbo3 with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 4.514367384001201e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 2\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/bertweet-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▇▄▁▅█</td></tr><tr><td>accuracy</td><td>▁▇▇▇█▇▇███</td></tr><tr><td>auprc</td><td>▁▅▆▇▇▆▇███</td></tr><tr><td>auroc</td><td>▁▅▇▇▇▇▇███</td></tr><tr><td>eval_loss</td><td>█▅▃▂▂▂▂▁▁▂</td></tr><tr><td>fn</td><td>▁█▆▅▆▅▅▆▅▆</td></tr><tr><td>fp</td><td>█▁▂▂▁▂▂▁▁▁</td></tr><tr><td>global_step</td><td>▁▂▂▃▃▄▄▅▅▅▆▆▇██</td></tr><tr><td>lr</td><td>█▆▄▃▁</td></tr><tr><td>mcc</td><td>▁▇▇▇▇▇▇███</td></tr><tr><td>tn</td><td>▁█▇▇█▇▇███</td></tr><tr><td>tp</td><td>█▁▃▄▃▄▄▃▄▃</td></tr><tr><td>train_loss</td><td>█▅▇▇▄▂▅▂▁▆</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.67424</td></tr><tr><td>accuracy</td><td>0.83396</td></tr><tr><td>auprc</td><td>0.84267</td></tr><tr><td>auroc</td><td>0.89018</td></tr><tr><td>eval_loss</td><td>0.44245</td></tr><tr><td>fn</td><td>37</td></tr><tr><td>fp</td><td>51</td></tr><tr><td>global_step</td><td>266</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.64508</td></tr><tr><td>tn</td><td>290</td></tr><tr><td>tp</td><td>152</td></tr><tr><td>train_loss</td><td>0.59942</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ni8jldg3 with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 4\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231104_121124-ni8jldg3</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/ni8jldg3' target=\"_blank\">silvery-sweep-5</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws</a>"
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/bertweet-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▅▄▃▅█▂▅▂▁▁</td></tr><tr><td>accuracy</td><td>▁▇▇▇▇█▇█▇██▇█▇█▇█████</td></tr><tr><td>auprc</td><td>▁▅▆▇▇███▇████████████</td></tr><tr><td>auroc</td><td>▁▅▇▇█████████████████</td></tr><tr><td>eval_loss</td><td>▇▄▂▂▁▁▁▁▃▄▂▃▇▅▄▄▇▇█▇▇</td></tr><tr><td>fn</td><td>▁▆▆▅▆▆▄▆▆▇▆▆█▆▆▅▇▆▆▆▆</td></tr><tr><td>fp</td><td>█▂▂▂▂▁▂▁▂▁▂▂▁▂▂▂▁▁▁▁▁</td></tr><tr><td>global_step</td><td>▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇███</td></tr><tr><td>lr</td><td>█▇▆▆▅▄▃▃▂▁</td></tr><tr><td>mcc</td><td>▁▆▇▇▇█▇▇▇█▇▇▇▇▇▇█████</td></tr><tr><td>tn</td><td>▁▇▇▇▇█▇█▇█▇▇█▇▇▇█████</td></tr><tr><td>tp</td><td>█▃▃▄▃▃▅▃▃▂▃▃▁▃▃▄▂▃▃▃▃</td></tr><tr><td>train_loss</td><td>▆▅▆▆▅▃▆▄▄█▃▂▅▂▄▁▅▁▃▄▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.02605</td></tr><tr><td>accuracy</td><td>0.82642</td></tr><tr><td>auprc</td><td>0.83037</td></tr><tr><td>auroc</td><td>0.88818</td></tr><tr><td>eval_loss</td><td>0.67138</td></tr><tr><td>fn</td><td>40</td></tr><tr><td>fp</td><td>52</td></tr><tr><td>global_step</td><td>532</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.62774</td></tr><tr><td>tn</td><td>289</td></tr><tr><td>tp</td><td>149</td></tr><tr><td>train_loss</td><td>0.01867</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: wpxipu7y with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 4.610787776858645e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 5\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231104_121656-wpxipu7y</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/wpxipu7y' target=\"_blank\">zany-sweep-6</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/bertweet-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▆▄▃▆█▂▄▂▁▁▁▁▂</td></tr><tr><td>accuracy</td><td>▁▇▇▇██▆▇▇████▇███▇█████████</td></tr><tr><td>auprc</td><td>▁▅▆▇▇▇████████▇████▇▇▇▇▇▇▇▇</td></tr><tr><td>auroc</td><td>▁▅▇▇▇██████▇▇█▇▇███▇▇▇▇▇▇▇▇</td></tr><tr><td>eval_loss</td><td>▄▃▂▂▁▁▂▁▁▁▂▃▄▂▆▄▄▄▅▆▆█▆█▇▇▇</td></tr><tr><td>fn</td><td>▁█▅▅▇▅▃▄▅▅▆▆▇▄█▅▆▅▅▆▆▇▆▇▆▆▆</td></tr><tr><td>fp</td><td>█▂▂▃▁▂▃▂▂▂▁▁▁▂▁▂▁▂▂▁▁▁▁▁▁▁▁</td></tr><tr><td>global_step</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇████</td></tr><tr><td>lr</td><td>█▇▇▆▆▅▅▄▃▃▂▂▁</td></tr><tr><td>mcc</td><td>▁▆▇▇▇▇▆▇▇███▇▇▇▇█▇█████████</td></tr><tr><td>tn</td><td>▁▇▇▆█▇▆▇▇▇███▇█▇█▇▇████████</td></tr><tr><td>tp</td><td>█▁▄▄▂▄▆▅▄▄▃▃▂▅▁▄▃▄▄▃▃▂▃▂▃▃▃</td></tr><tr><td>train_loss</td><td>█▆▇█▇▄█▅▅▇▄▂▆▅▆▁▄▁▄▂▁▂▂▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.18691</td></tr><tr><td>accuracy</td><td>0.82264</td></tr><tr><td>auprc</td><td>0.78541</td></tr><tr><td>auroc</td><td>0.8661</td></tr><tr><td>eval_loss</td><td>0.86812</td></tr><tr><td>fn</td><td>47</td></tr><tr><td>fp</td><td>47</td></tr><tr><td>global_step</td><td>665</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.61349</td></tr><tr><td>tn</td><td>294</td></tr><tr><td>tp</td><td>142</td></tr><tr><td>train_loss</td><td>0.01158</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: t2s9w5nf with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 3\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/t2s9w5nf' target=\"_blank\">serene-sweep-7</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/bertweet-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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      "INFO:simpletransformers.classification.classification_utils: Saving features into cached file cache_dir/cached_dev_bertweet_512_2_2\n",
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     ]
    },
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▆▃▃▄█▁▃</td></tr><tr><td>accuracy</td><td>▁▂█▇█▇▇█████████</td></tr><tr><td>auprc</td><td>▁▅▆▇▇▇██████████</td></tr><tr><td>auroc</td><td>▁▅▇▇▇███████████</td></tr><tr><td>eval_loss</td><td>█▆▃▃▂▂▂▁▂▁▁▃▄▃▄▄</td></tr><tr><td>fn</td><td>▁▂█▅▇▄▄▆▇▅▅██▇▇▇</td></tr><tr><td>fp</td><td>█▇▁▃▁▂▂▁▁▂▂▁▁▁▁▁</td></tr><tr><td>global_step</td><td>▁▁▂▂▂▃▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇██</td></tr><tr><td>lr</td><td>█▇▆▅▃▂▁</td></tr><tr><td>mcc</td><td>▁▂█▇▇▇▇█████████</td></tr><tr><td>tn</td><td>▁▂█▆█▇▇██▇▇█████</td></tr><tr><td>tp</td><td>█▇▁▄▂▅▅▃▂▄▄▁▁▂▂▂</td></tr><tr><td>train_loss</td><td>█▆▇█▆▄▆▄▂▅▄▁▅▃▆▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.31088</td></tr><tr><td>accuracy</td><td>0.81509</td></tr><tr><td>auprc</td><td>0.85397</td></tr><tr><td>auroc</td><td>0.88865</td></tr><tr><td>eval_loss</td><td>0.48713</td></tr><tr><td>fn</td><td>40</td></tr><tr><td>fp</td><td>58</td></tr><tr><td>global_step</td><td>399</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.60702</td></tr><tr><td>tn</td><td>283</td></tr><tr><td>tp</td><td>149</td></tr><tr><td>train_loss</td><td>0.10764</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: y9gm6ajc with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.7855307688053472e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 2\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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       "Run data is saved locally in <code>C:\\Users\\mikeb\\OneDrive - The Pennsylvania State University\\Stance Detection\\wandb\\run-20231104_122812-y9gm6ajc</code>"
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       "Resuming run <strong><a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/y9gm6ajc' target=\"_blank\">bright-sweep-8</a></strong> to <a href='https://wandb.ai/mlburnham/trump-BERTweet2' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/sweeps/982tyfws</a>"
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     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/bertweet-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▆▂▁▄█</td></tr><tr><td>accuracy</td><td>▁▁███▇▇▇██</td></tr><tr><td>auprc</td><td>▁▅▆▇▇█▇███</td></tr><tr><td>auroc</td><td>▁▆▇▇██████</td></tr><tr><td>eval_loss</td><td>█▆▂▂▂▂▂▁▁▁</td></tr><tr><td>fn</td><td>▁▁██▇▇▅▅▇█</td></tr><tr><td>fp</td><td>██▁▁▁▂▂▂▁▁</td></tr><tr><td>global_step</td><td>▁▂▂▃▃▄▄▅▅▅▆▆▇██</td></tr><tr><td>lr</td><td>█▆▅▃▁</td></tr><tr><td>mcc</td><td>▁▂███▇▇▇██</td></tr><tr><td>tn</td><td>▁▁███▇▇▇██</td></tr><tr><td>tp</td><td>██▁▁▂▂▄▄▂▁</td></tr><tr><td>train_loss</td><td>▇▄▇█▄▃▅▄▁▄</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.78189</td></tr><tr><td>accuracy</td><td>0.82642</td></tr><tr><td>auprc</td><td>0.85209</td></tr><tr><td>auroc</td><td>0.89512</td></tr><tr><td>eval_loss</td><td>0.41143</td></tr><tr><td>fn</td><td>42</td></tr><tr><td>fp</td><td>50</td></tr><tr><td>global_step</td><td>266</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.62558</td></tr><tr><td>tn</td><td>291</td></tr><tr><td>tp</td><td>147</td></tr><tr><td>train_loss</td><td>0.47763</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: cmgywezx with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 3\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/bertweet-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▅▄▃▅█▁▃</td></tr><tr><td>accuracy</td><td>▁▇█▇█▇▇█████████</td></tr><tr><td>auprc</td><td>▁▅▇▇▇▇▇█████████</td></tr><tr><td>auroc</td><td>▁▅▇▇█▇██████████</td></tr><tr><td>eval_loss</td><td>█▄▂▂▁▁▁▁▁▁▁▅▇▄▄▄</td></tr><tr><td>fn</td><td>▁█▇▇▇▆▅▇▆▆▆██▆▆▆</td></tr><tr><td>fp</td><td>█▂▂▂▁▂▂▁▂▂▂▁▁▁▁▁</td></tr><tr><td>global_step</td><td>▁▁▂▂▂▃▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇██</td></tr><tr><td>lr</td><td>█▇▆▄▃▂▁</td></tr><tr><td>mcc</td><td>▁▇▇▇█▇▇█████████</td></tr><tr><td>tn</td><td>▁▇▇▇█▇▇█▇▇▇█████</td></tr><tr><td>tp</td><td>█▁▂▂▂▃▅▂▃▃▃▁▁▃▃▃</td></tr><tr><td>train_loss</td><td>█▆▇█▄▄▆▄▄▃▂▁▃▂▄▃</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.34867</td></tr><tr><td>accuracy</td><td>0.81698</td></tr><tr><td>auprc</td><td>0.83826</td></tr><tr><td>auroc</td><td>0.88579</td></tr><tr><td>eval_loss</td><td>0.53319</td></tr><tr><td>fn</td><td>39</td></tr><tr><td>fp</td><td>58</td></tr><tr><td>global_step</td><td>399</td></tr><tr><td>lr</td><td>1e-05</td></tr><tr><td>mcc</td><td>0.61169</td></tr><tr><td>tn</td><td>283</td></tr><tr><td>tp</td><td>150</td></tr><tr><td>train_loss</td><td>0.20986</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">brisk-sweep-9</strong> at: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/cmgywezx' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/runs/cmgywezx</a><br/>Synced 3 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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       "Find logs at: <code>.\\wandb\\run-20231104_123126-cmgywezx\\logs</code>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tnum_train_epochs: 2\n",
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      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/bertweet-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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      "Epoch 2 of 2: 100%|██████████████████████████████| 2/2 [02:37<00:00, 78.96s/it]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>▆▆▁▄█</td></tr><tr><td>accuracy</td><td>▁▇█▄▇█▇███</td></tr><tr><td>auprc</td><td>▁▅▇▁▆▆▇███</td></tr><tr><td>auroc</td><td>▁▅▇▄▆▇████</td></tr><tr><td>eval_loss</td><td>█▃▂▇▃▂▁▁▁▁</td></tr><tr><td>fn</td><td>▁█▇▂█▆▅▆▇▇</td></tr><tr><td>fp</td><td>█▂▁▆▁▂▂▁▁▁</td></tr><tr><td>global_step</td><td>▁▂▂▃▃▄▄▅▅▅▆▆▇██</td></tr><tr><td>lr</td><td>█▆▅▃▁</td></tr><tr><td>mcc</td><td>▁▇█▄▇▇▇███</td></tr><tr><td>tn</td><td>▁▇█▃█▇▇███</td></tr><tr><td>tp</td><td>█▁▂▇▁▃▄▃▂▂</td></tr><tr><td>train_loss</td><td>█▄▇█▂▁▆▂▁█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Training loss</td><td>0.79746</td></tr><tr><td>accuracy</td><td>0.80377</td></tr><tr><td>auprc</td><td>0.82171</td></tr><tr><td>auroc</td><td>0.87915</td></tr><tr><td>eval_loss</td><td>0.44901</td></tr><tr><td>fn</td><td>40</td></tr><tr><td>fp</td><td>64</td></tr><tr><td>global_step</td><td>266</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>mcc</td><td>0.58685</td></tr><tr><td>tn</td><td>277</td></tr><tr><td>tp</td><td>149</td></tr><tr><td>train_loss</td><td>0.72208</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">helpful-sweep-10</strong> at: <a href='https://wandb.ai/mlburnham/trump-BERTweet2/runs/q8m3ahku' target=\"_blank\">https://wandb.ai/mlburnham/trump-BERTweet2/runs/q8m3ahku</a><br/>Synced 3 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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     "data": {
      "text/html": [
       "Find logs at: <code>.\\wandb\\run-20231104_123535-q8m3ahku\\logs</code>"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "sweep_config = {\n",
    "    \"method\": \"bayes\",\n",
    "    \"metric\": {\"name\": \"mcc\", \"goal\": \"maximize\"},\n",
    "    \"parameters\": {\n",
    "        \"num_train_epochs\": {\"min\": 2, \"max\": 5},\n",
    "        \"learning_rate\": {\"min\": 0.0, \"max\": 5e-05 },\n",
    "    },\n",
    "}\n",
    "\n",
    "sweep_id = wandb.sweep(sweep_config, project=\"trump-BERTweet2\")\n",
    "\n",
    "# logging\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "transformers_logger = logging.getLogger(\"transformers\")\n",
    "transformers_logger.setLevel(logging.WARNING)\n",
    "\n",
    "# args\n",
    "model_args = ClassificationArgs()\n",
    "model_args.evaluate_during_training = True\n",
    "model_args.evaluate_during_training_silent = False\n",
    "model_args.evaluate_during_training_steps = 30\n",
    "model_args.manual_seed = 1\n",
    "model_args.max_seq_length = 512\n",
    "model_args.save_eval_checkpoints = False\n",
    "model_args.save_model_every_epoch = True\n",
    "model_args.overwrite_output_dir = True\n",
    "model_args.reprocess_input_data = True\n",
    "model_args.train_batch_size = 16\n",
    "model_args.eval_batch_size = 16\n",
    "model_args.train_custom_parameters_only = False\n",
    "model_args.wandb_project = \"trump-BERTweet2\"\n",
    "\n",
    "# training function\n",
    "def train():\n",
    "    # Initialize a new wandb run\n",
    "    wandb.init(resume = True)\n",
    "\n",
    "    # Create a TransformerModel\n",
    "    model = ClassificationModel(\n",
    "        \"bertweet\",\n",
    "        \"vinai/bertweet-base\",\n",
    "        weight = [1,3],\n",
    "        use_cuda=True,\n",
    "        args=model_args,\n",
    "        sweep_config=wandb.config,\n",
    "    )\n",
    "\n",
    "    # Train the model\n",
    "    model.train_model(\n",
    "        train_set,\n",
    "        eval_df=val_set,\n",
    "        verbose = False,\n",
    "        accuracy=lambda truth, predictions: accuracy_score(\n",
    "            truth, [round(p) for p in predictions]\n",
    "        ),\n",
    "    )\n",
    "    # Sync wandb\n",
    "    wandb.join()\n",
    "\n",
    "# train\n",
    "wandb.agent(sweep_id, train, count = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47098bd-df2f-4d0d-999e-ddf619ed1c36",
   "metadata": {},
   "source": [
    "## DeBERTa Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b579da45-cede-47a8-adf8-6053851febde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the name of the model on the HuggingFace directory\n",
    "modname = \"microsoft/deberta-v3-base\"\n",
    "# the directory where you want the maodel checkpoints to save\n",
    "training_directory ='deberta_sup'\n",
    "# use GPU if one is available, else CPU. You will want GPU access for training.\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(f\"Device: {device}\")\n",
    "\n",
    "# convert the data to a huggingface dataset for ease of use\n",
    "tr_ds = Dataset.from_pandas(train_set)\n",
    "val_ds = Dataset.from_pandas(val_set)\n",
    "ds = DatasetDict()\n",
    "ds['train'] = tr_ds\n",
    "ds['validate'] = val_ds\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(modname)\n",
    "\n",
    "# define a generic tokenizing function\n",
    "def tokenize_function(docs):\n",
    "    return tokenizer(docs['text'], padding = 'max_length', truncation = True)\n",
    "\n",
    "# tokenize the dataset\n",
    "dstok = ds.map(tokenize_function)\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained(modname, num_labels = 2, ignore_mismatched_sizes=True)\n",
    "\n",
    "training_args = TrainingArguments(output_dir=training_directory,\n",
    "    logging_dir=f'{training_directory}/logs',\n",
    "    lr_scheduler_type= \"linear\", # how to adjust the learning rate while training\n",
    "    group_by_length=False,  # can increase speed with dynamic padding, by grouping similar length texts https://huggingface.co/transformers/main_classes/trainer.html\n",
    "    learning_rate = 2e-5, # the initial learning rate\n",
    "    per_device_train_batch_size = 16, # lower the batch size if you are running out of memory\n",
    "    per_device_eval_batch_size = 16,\n",
    "    gradient_accumulation_steps= 1,  # Number of batches to pass through the model before updating the weights. Can be useful when using very small batch sizes like 2 or 4.\n",
    "    num_train_epochs=6, # number of times to pass the entire training set through the model\n",
    "    warmup_ratio=0.06, # warmup length before learning rate scheduler kicks in\n",
    "    weight_decay=0.01, # weight regularization\n",
    "    fp16=True,\n",
    "    fp16_full_eval=True,\n",
    "    evaluation_strategy=\"epoch\", # evaluate the model every n steps or epochs.\n",
    "    seed=1,\n",
    "    #eval_steps=50,  # how many steps between evaluations if using steps evaluation strategy. 1 step = 1 gradient update\n",
    "    save_strategy=\"epoch\",  # Save after each epoch or after n steps\n",
    "    #save_steps=100,  # Number of updates steps before two checkpoint saves.\n",
    "    dataloader_num_workers = 1, # number of cpu workers passing data to the the GPU\n",
    ")\n",
    "\n",
    "# function for computing metrics\n",
    "def compute_metrics_standard(eval_pred, label_text_alphabetical=list(model.config.id2label.values())):\n",
    "    labels = eval_pred.label_ids\n",
    "    pred_logits = eval_pred.predictions\n",
    "    preds_max = np.argmax(pred_logits, axis=1)\n",
    "\n",
    "    # metrics\n",
    "    precision_macro, recall_macro, f1_macro, _ = precision_recall_fscore_support(labels, preds_max, average='macro')\n",
    "    precision_micro, recall_micro, f1_micro, _ = precision_recall_fscore_support(labels, preds_max, average='micro')\n",
    "    acc_balanced = balanced_accuracy_score(labels, preds_max)\n",
    "    acc_not_balanced = accuracy_score(labels, preds_max)\n",
    "\n",
    "    metrics = {'f1_macro': f1_macro,\n",
    "            'f1_micro': f1_micro,\n",
    "            'accuracy_balanced': acc_balanced,\n",
    "            'accuracy': acc_not_balanced,\n",
    "            'precision_macro': precision_macro,\n",
    "            'recall_macro': recall_macro,\n",
    "            'precision_micro': precision_micro,\n",
    "            'recall_micro': recall_micro,\n",
    "            }\n",
    "    print(\"Aggregate metrics: \", {key: metrics[key] for key in metrics if key not in [\"label_gold_raw\", \"label_predicted_raw\"]} )  # print metrics but without label lists\n",
    "    print(\"Detailed metrics: \", classification_report(\n",
    "        labels, preds_max, labels=np.sort(pd.factorize(label_text_alphabetical, sort=True)[0]),\n",
    "        target_names=label_text_alphabetical, sample_weight=None,\n",
    "        digits=2, output_dict=True, zero_division='warn'),\n",
    "    \"\\n\")\n",
    "\n",
    "    return metrics\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    "    train_dataset=dstok['train'],\n",
    "    eval_dataset=dstok['validate'],\n",
    "    compute_metrics=lambda x: compute_metrics_standard(x, label_text_alphabetical=list(model.config.id2label.values()))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e1b4fe1b-9afd-4ccb-b045-f04b8bbfba00",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'learning_rate' was locked by 'sweep' (ignored update).\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'num_train_epochs' was locked by 'sweep' (ignored update).\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='799' max='1064' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [ 799/1064 03:19 < 01:06, 4.00 it/s, Epoch 6/8]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>F1 Macro</th>\n",
       "      <th>F1 Micro</th>\n",
       "      <th>Accuracy Balanced</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision Macro</th>\n",
       "      <th>Recall Macro</th>\n",
       "      <th>Precision Micro</th>\n",
       "      <th>Recall Micro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.482102</td>\n",
       "      <td>0.802675</td>\n",
       "      <td>0.828302</td>\n",
       "      <td>0.789919</td>\n",
       "      <td>0.828302</td>\n",
       "      <td>0.827807</td>\n",
       "      <td>0.789919</td>\n",
       "      <td>0.828302</td>\n",
       "      <td>0.828302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.817799</td>\n",
       "      <td>0.797730</td>\n",
       "      <td>0.822642</td>\n",
       "      <td>0.786700</td>\n",
       "      <td>0.822642</td>\n",
       "      <td>0.817892</td>\n",
       "      <td>0.786700</td>\n",
       "      <td>0.822642</td>\n",
       "      <td>0.822642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.916391</td>\n",
       "      <td>0.791338</td>\n",
       "      <td>0.820755</td>\n",
       "      <td>0.776979</td>\n",
       "      <td>0.820755</td>\n",
       "      <td>0.823998</td>\n",
       "      <td>0.776979</td>\n",
       "      <td>0.820755</td>\n",
       "      <td>0.820755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.180300</td>\n",
       "      <td>0.969867</td>\n",
       "      <td>0.771650</td>\n",
       "      <td>0.781132</td>\n",
       "      <td>0.787460</td>\n",
       "      <td>0.781132</td>\n",
       "      <td>0.767720</td>\n",
       "      <td>0.787460</td>\n",
       "      <td>0.781132</td>\n",
       "      <td>0.781132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.180300</td>\n",
       "      <td>1.068731</td>\n",
       "      <td>0.773688</td>\n",
       "      <td>0.784906</td>\n",
       "      <td>0.785675</td>\n",
       "      <td>0.784906</td>\n",
       "      <td>0.768934</td>\n",
       "      <td>0.785675</td>\n",
       "      <td>0.784906</td>\n",
       "      <td>0.784906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.180300</td>\n",
       "      <td>1.164034</td>\n",
       "      <td>0.804140</td>\n",
       "      <td>0.824528</td>\n",
       "      <td>0.797600</td>\n",
       "      <td>0.824528</td>\n",
       "      <td>0.813399</td>\n",
       "      <td>0.797600</td>\n",
       "      <td>0.824528</td>\n",
       "      <td>0.824528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggregate metrics:  {'f1_macro': 0.8026749147979494, 'f1_micro': 0.8283018867924529, 'accuracy_balanced': 0.7899191608869027, 'accuracy': 0.8283018867924529, 'precision_macro': 0.8278070175438597, 'recall_macro': 0.7899191608869027, 'precision_micro': 0.8283018867924529, 'recall_micro': 0.8283018867924529}\n",
      "Detailed metrics:  {'LABEL_0': {'precision': 0.8289473684210527, 'recall': 0.9237536656891495, 'f1-score': 0.8737864077669902, 'support': 341.0}, 'LABEL_1': {'precision': 0.8266666666666667, 'recall': 0.656084656084656, 'f1-score': 0.7315634218289085, 'support': 189.0}, 'accuracy': 0.8283018867924529, 'macro avg': {'precision': 0.8278070175438597, 'recall': 0.7899191608869027, 'f1-score': 0.8026749147979494, 'support': 530.0}, 'weighted avg': {'precision': 0.8281340615690169, 'recall': 0.8283018867924529, 'f1-score': 0.8230691542909573, 'support': 530.0}} \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_35560\\3886566834.py:23: FutureWarning: factorize with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  labels, preds_max, labels=np.sort(pd.factorize(label_text_alphabetical, sort=True)[0]),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggregate metrics:  {'f1_macro': 0.7977296349226972, 'f1_micro': 0.8226415094339623, 'accuracy_balanced': 0.7866995608931093, 'accuracy': 0.8226415094339623, 'precision_macro': 0.8178924731182795, 'recall_macro': 0.7866995608931093, 'precision_micro': 0.8226415094339623, 'recall_micro': 0.8226415094339623}\n",
      "Detailed metrics:  {'LABEL_0': {'precision': 0.8293333333333334, 'recall': 0.9120234604105572, 'f1-score': 0.8687150837988827, 'support': 341.0}, 'LABEL_1': {'precision': 0.8064516129032258, 'recall': 0.6613756613756614, 'f1-score': 0.7267441860465116, 'support': 189.0}, 'accuracy': 0.8226415094339623, 'macro avg': {'precision': 0.8178924731182795, 'recall': 0.7866995608931093, 'f1-score': 0.7977296349226972, 'support': 530.0}, 'weighted avg': {'precision': 0.8211736254818421, 'recall': 0.8226415094339623, 'f1-score': 0.8180877259211504, 'support': 530.0}} \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_35560\\3886566834.py:23: FutureWarning: factorize with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  labels, preds_max, labels=np.sort(pd.factorize(label_text_alphabetical, sort=True)[0]),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggregate metrics:  {'f1_macro': 0.7913377179350101, 'f1_micro': 0.8207547169811321, 'accuracy_balanced': 0.776978696333535, 'accuracy': 0.8207547169811321, 'precision_macro': 0.8239981123856541, 'recall_macro': 0.776978696333535, 'precision_micro': 0.8207547169811321, 'recall_micro': 0.8207547169811321}\n",
      "Detailed metrics:  {'LABEL_0': {'precision': 0.8170103092783505, 'recall': 0.9296187683284457, 'f1-score': 0.869684499314129, 'support': 341.0}, 'LABEL_1': {'precision': 0.8309859154929577, 'recall': 0.6243386243386243, 'f1-score': 0.7129909365558912, 'support': 189.0}, 'accuracy': 0.8207547169811321, 'macro avg': {'precision': 0.8239981123856541, 'recall': 0.776978696333535, 'f1-score': 0.7913377179350101, 'support': 530.0}, 'weighted avg': {'precision': 0.8219940631926161, 'recall': 0.8207547169811321, 'f1-score': 0.8138069835380782, 'support': 530.0}} \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_35560\\3886566834.py:23: FutureWarning: factorize with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  labels, preds_max, labels=np.sort(pd.factorize(label_text_alphabetical, sort=True)[0]),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggregate metrics:  {'f1_macro': 0.7716501507970703, 'f1_micro': 0.7811320754716982, 'accuracy_balanced': 0.787459851975981, 'accuracy': 0.7811320754716982, 'precision_macro': 0.76772011965145, 'recall_macro': 0.787459851975981, 'precision_micro': 0.7811320754716982, 'recall_micro': 0.7811320754716982}\n",
      "Detailed metrics:  {'LABEL_0': {'precision': 0.8787878787878788, 'recall': 0.7653958944281525, 'f1-score': 0.8181818181818182, 'support': 341.0}, 'LABEL_1': {'precision': 0.6566523605150214, 'recall': 0.8095238095238095, 'f1-score': 0.7251184834123223, 'support': 189.0}, 'accuracy': 0.7811320754716982, 'macro avg': {'precision': 0.76772011965145, 'recall': 0.787459851975981, 'f1-score': 0.7716501507970703, 'support': 530.0}, 'weighted avg': {'precision': 0.7995735147245392, 'recall': 0.7811320754716982, 'f1-score': 0.7849950818206205, 'support': 530.0}} \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_35560\\3886566834.py:23: FutureWarning: factorize with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  labels, preds_max, labels=np.sort(pd.factorize(label_text_alphabetical, sort=True)[0]),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggregate metrics:  {'f1_macro': 0.7736875224739302, 'f1_micro': 0.7849056603773585, 'accuracy_balanced': 0.7856754953529147, 'accuracy': 0.7849056603773585, 'precision_macro': 0.768934137684229, 'recall_macro': 0.7856754953529147, 'precision_micro': 0.7849056603773585, 'recall_micro': 0.7849056603773585}\n",
      "Detailed metrics:  {'LABEL_0': {'precision': 0.8697068403908795, 'recall': 0.782991202346041, 'f1-score': 0.824074074074074, 'support': 341.0}, 'LABEL_1': {'precision': 0.6681614349775785, 'recall': 0.7883597883597884, 'f1-score': 0.7233009708737865, 'support': 189.0}, 'accuracy': 0.7849056603773585, 'macro avg': {'precision': 0.768934137684229, 'recall': 0.7856754953529147, 'f1-score': 0.7736875224739302, 'support': 530.0}, 'weighted avg': {'precision': 0.7978349882717967, 'recall': 0.7849056603773585, 'f1-score': 0.7881380051969904, 'support': 530.0}} \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_35560\\3886566834.py:23: FutureWarning: factorize with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  labels, preds_max, labels=np.sort(pd.factorize(label_text_alphabetical, sort=True)[0]),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggregate metrics:  {'f1_macro': 0.804139728759949, 'f1_micro': 0.8245283018867926, 'accuracy_balanced': 0.7975996524383622, 'accuracy': 0.8245283018867925, 'precision_macro': 0.8133986928104575, 'recall_macro': 0.7975996524383622, 'precision_micro': 0.8245283018867925, 'recall_micro': 0.8245283018867925}\n",
      "Detailed metrics:  {'LABEL_0': {'precision': 0.8444444444444444, 'recall': 0.8914956011730205, 'f1-score': 0.8673323823109843, 'support': 341.0}, 'LABEL_1': {'precision': 0.7823529411764706, 'recall': 0.7037037037037037, 'f1-score': 0.7409470752089137, 'support': 189.0}, 'accuracy': 0.8245283018867925, 'macro avg': {'precision': 0.8133986928104575, 'recall': 0.7975996524383622, 'f1-score': 0.804139728759949, 'support': 530.0}, 'weighted avg': {'precision': 0.8223023800715256, 'recall': 0.8245283018867925, 'f1-score': 0.8222629048726988, 'support': 530.0}} \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_35560\\3886566834.py:23: FutureWarning: factorize with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  labels, preds_max, labels=np.sort(pd.factorize(label_text_alphabetical, sort=True)[0]),\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "[enforce fail at C:\\cb\\pytorch_1000000000000\\work\\caffe2\\serialize\\inline_container.cc:337] . unexpected pos 752202560 vs 752202448",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "File \u001b[1;32m~\\.conda\\envs\\sandbox\\Lib\\site-packages\\torch\\serialization.py:441\u001b[0m, in \u001b[0;36msave\u001b[1;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization)\u001b[0m\n\u001b[0;32m    440\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _open_zipfile_writer(f) \u001b[38;5;28;01mas\u001b[39;00m opened_zipfile:\n\u001b[1;32m--> 441\u001b[0m     \u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopened_zipfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpickle_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpickle_protocol\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    442\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n",
      "File \u001b[1;32m~\\.conda\\envs\\sandbox\\Lib\\site-packages\\torch\\serialization.py:668\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(obj, zip_file, pickle_module, pickle_protocol)\u001b[0m\n\u001b[0;32m    667\u001b[0m num_bytes \u001b[38;5;241m=\u001b[39m storage\u001b[38;5;241m.\u001b[39mnbytes()\n\u001b[1;32m--> 668\u001b[0m \u001b[43mzip_file\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_record\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_ptr\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_bytes\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: [enforce fail at C:\\cb\\pytorch_1000000000000\\work\\caffe2\\serialize\\inline_container.cc:476] . PytorchStreamWriter failed writing file data/2: file write failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[26], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\sandbox\\Lib\\site-packages\\transformers\\trainer.py:1555\u001b[0m, in \u001b[0;36mTrainer.train\u001b[1;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[0;32m   1553\u001b[0m         hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[0;32m   1554\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1555\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1556\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1557\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1558\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1559\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1560\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\sandbox\\Lib\\site-packages\\transformers\\trainer.py:1937\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[1;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[0;32m   1934\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol\u001b[38;5;241m.\u001b[39mshould_training_stop \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m   1936\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_epoch_end(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n\u001b[1;32m-> 1937\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_log_save_evaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtr_loss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1939\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m DebugOption\u001b[38;5;241m.\u001b[39mTPU_METRICS_DEBUG \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mdebug:\n\u001b[0;32m   1940\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_torch_tpu_available():\n\u001b[0;32m   1941\u001b[0m         \u001b[38;5;66;03m# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)\u001b[39;00m\n",
      "File \u001b[1;32m~\\.conda\\envs\\sandbox\\Lib\\site-packages\\transformers\\trainer.py:2282\u001b[0m, in \u001b[0;36mTrainer._maybe_log_save_evaluate\u001b[1;34m(self, tr_loss, model, trial, epoch, ignore_keys_for_eval)\u001b[0m\n\u001b[0;32m   2279\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlr_scheduler\u001b[38;5;241m.\u001b[39mstep(metrics[metric_to_check])\n\u001b[0;32m   2281\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol\u001b[38;5;241m.\u001b[39mshould_save:\n\u001b[1;32m-> 2282\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_save_checkpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetrics\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2283\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_save(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n",
      "File \u001b[1;32m~\\.conda\\envs\\sandbox\\Lib\\site-packages\\transformers\\trainer.py:2387\u001b[0m, in \u001b[0;36mTrainer._save_checkpoint\u001b[1;34m(self, model, trial, metrics)\u001b[0m\n\u001b[0;32m   2379\u001b[0m         smp\u001b[38;5;241m.\u001b[39msave(\n\u001b[0;32m   2380\u001b[0m             opt_state_dict,\n\u001b[0;32m   2381\u001b[0m             os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(output_dir, OPTIMIZER_NAME),\n\u001b[0;32m   2382\u001b[0m             partial\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m   2383\u001b[0m             v3\u001b[38;5;241m=\u001b[39msmp\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mcfg\u001b[38;5;241m.\u001b[39mshard_optimizer_state,\n\u001b[0;32m   2384\u001b[0m         )\n\u001b[0;32m   2385\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mshould_save \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_deepspeed_enabled \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfsdp \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_fsdp_enabled):\n\u001b[0;32m   2386\u001b[0m     \u001b[38;5;66;03m# deepspeed.save_checkpoint above saves model/optim/sched\u001b[39;00m\n\u001b[1;32m-> 2387\u001b[0m     \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstate_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mOPTIMIZER_NAME\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2389\u001b[0m \u001b[38;5;66;03m# Save SCHEDULER & SCALER\u001b[39;00m\n\u001b[0;32m   2390\u001b[0m is_deepspeed_custom_scheduler \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_deepspeed_enabled \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[0;32m   2391\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlr_scheduler, DeepSpeedSchedulerWrapper\n\u001b[0;32m   2392\u001b[0m )\n",
      "File \u001b[1;32m~\\.conda\\envs\\sandbox\\Lib\\site-packages\\torch\\serialization.py:440\u001b[0m, in \u001b[0;36msave\u001b[1;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization)\u001b[0m\n\u001b[0;32m    437\u001b[0m _check_save_filelike(f)\n\u001b[0;32m    439\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _use_new_zipfile_serialization:\n\u001b[1;32m--> 440\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_open_zipfile_writer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mopened_zipfile\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m    441\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopened_zipfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpickle_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpickle_protocol\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    442\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mreturn\u001b[39;49;00m\n",
      "File \u001b[1;32m~\\.conda\\envs\\sandbox\\Lib\\site-packages\\torch\\serialization.py:291\u001b[0m, in \u001b[0;36m_open_zipfile_writer_file.__exit__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    290\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 291\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfile_like\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_end_of_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: [enforce fail at C:\\cb\\pytorch_1000000000000\\work\\caffe2\\serialize\\inline_container.cc:337] . unexpected pos 752202560 vs 752202448"
     ]
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ad15e4-8e3b-4401-a163-d3a0a7fadd04",
   "metadata": {},
   "source": [
    "### Best model prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c63855c6-aed5-405e-9df6-30c39138c5dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "from sklearn.metrics import matthews_corrcoef"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "283e51bd-6821-452d-88c7-e9745097bccd",
   "metadata": {},
   "source": [
    "Polibert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "540db616-b67c-4592-8cf6-3b6693a10c94",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6833719329086678"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = pipeline('text-classification', model=\"./outputs/best_polibert\", device = 0)\n",
    "\n",
    "res = pipe(list(test_df['text']))\n",
    "\n",
    "labels = [int(label['label'][-1]) for label in res]\n",
    "\n",
    "test_df['polibert'] = labels\n",
    "\n",
    "matthews_corrcoef(test_df['labels'], test_df['polibert'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab06bd0-d93f-4ada-932c-05043e6ee609",
   "metadata": {},
   "source": [
    "Roberta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e66df4af-cb08-499d-a3dd-d48520883166",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import RobertaTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eafb4936-47a9-4e44-9a4b-09828c885738",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = RobertaTokenizer.from_pretrained('roberta-base', force_download=True)\n",
    "\n",
    "pipe = pipeline('text-classification', model=\"./outputs/best_roberta\", tokenizer = tokenizer, device = 0)\n",
    "\n",
    "res = pipe(list(test_df['text']))\n",
    "\n",
    "labels = [int(label['label'][-1]) for label in res]\n",
    "\n",
    "test_df['roberta'] = labels\n",
    "\n",
    "matthews_corrcoef(test_df['labels'], test_df['roberta'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca412ad-f00a-40e4-a16c-8657d79536c9",
   "metadata": {},
   "source": [
    "BERTweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aea081d1-012f-419c-959a-ba459cbe0f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = pipeline('text-classification', model='outputs/best_bertweet', device = 0)\n",
    "\n",
    "res = pipe(list(test_df['text']))\n",
    "\n",
    "labels = [int(label['label'][-1]) for label in res]\n",
    "\n",
    "test_df['bertweet'] = labels\n",
    "\n",
    "matthews_corrcoef(test_df['labels'], test_df['bertweet'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a98d86a-5f61-4895-b6bf-762c5c7bdb6d",
   "metadata": {},
   "source": [
    "DeBERTa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2671ef01-f28b-4a34-8716-6b9f69a7a05d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6325276372798002"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = pipeline('text-classification', model='outputs/best_deberta', device = 0)\n",
    "\n",
    "res = pipe(list(test_df['text']))\n",
    "\n",
    "labels = [int(label['label'][-1]) for label in res]\n",
    "\n",
    "test_df['deberta'] = labels\n",
    "\n",
    "matthews_corrcoef(test_df['labels'], test_df['deberta'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c349f2e5-9c91-4093-91d5-ca12cccdaa60",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.to_csv('trump_twitter_supervised.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b97bfab-1682-4c84-85d6-0a38f8883871",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5541c204-62e8-4f5c-a788-62e0e6c1d36a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('trump_twitter_supervised.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5ebf4694-3b4d-4d6d-9c44-62c43831bbba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5107125122130706\n",
      "0.4895905212907938\n",
      "0.4942677833297822\n",
      "0.5994978679351038\n",
      "0.6328612319472\n",
      "0.6833719329086678\n",
      "0.6325276372798002\n",
      "0.6792853332207079\n"
     ]
    }
   ],
   "source": [
    "print(matthews_corrcoef(test_df['labels'], test_df['logistic']))\n",
    "print(matthews_corrcoef(test_df['labels'], test_df['forest']))\n",
    "print(matthews_corrcoef(test_df['labels'], test_df['svm']))\n",
    "print(matthews_corrcoef(test_df['labels'], test_df['roberta']))\n",
    "print(matthews_corrcoef(test_df['labels'], test_df['bertweet']))\n",
    "print(matthews_corrcoef(test_df['labels'], test_df['polibert']))\n",
    "print(matthews_corrcoef(test_df['labels'], test_df['deberta']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cdec031d-c1ff-4035-834f-55380389e228",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6733668341708542\n",
      "0.6234482758620691\n",
      "0.6800947867298578\n",
      "0.7545454545454546\n",
      "0.7701283547257877\n",
      "0.7980997624703089\n",
      "0.7582822085889571\n",
      "0.7861557478368356\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(test_df['labels'], test_df['logistic']))\n",
    "print(f1_score(test_df['labels'], test_df['forest']))\n",
    "print(f1_score(test_df['labels'], test_df['svm']))\n",
    "print(f1_score(test_df['labels'], test_df['roberta']))\n",
    "print(f1_score(test_df['labels'], test_df['bertweet']))\n",
    "print(f1_score(test_df['labels'], test_df['polibert']))\n",
    "print(f1_score(test_df['labels'], test_df['deberta']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d6074a6c-165c-44e5-a26e-d6f0372b185d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7709251101321586\n",
      "0.7594713656387665\n",
      "0.762114537444934\n",
      "0.8096916299559471\n",
      "0.826431718061674\n",
      "0.8502202643171806\n",
      "0.826431718061674\n",
      "0.8475770925110132\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(test_df['labels'], test_df['logistic']))\n",
    "print(accuracy_score(test_df['labels'], test_df['forest']))\n",
    "print(accuracy_score(test_df['labels'], test_df['svm']))\n",
    "print(accuracy_score(test_df['labels'], test_df['roberta']))\n",
    "print(accuracy_score(test_df['labels'], test_df['bertweet']))\n",
    "print(accuracy_score(test_df['labels'], test_df['polibert']))\n",
    "print(accuracy_score(test_df['labels'], test_df['deberta']))"
   ]
  }
 ],
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